Philosophers have recently focused on critical, epistemological challenges
that arise from the opacity of deep neural networks. One might conclude from
this literature that doing good science with opaque models is exceptionally
challenging, if not impossible. Yet, this is hard to square with the recent
boom in optimism for AI in science alongside a flood of recent scientific
breakthroughs driven by AI methods. In this paper, I argue that the disconnect
between philosophical pessimism and scientific optimism is driven by a failure
to examine how AI is actually used in science. I show that, in order to
understand the epistemic justification for AI-powered breakthroughs,
philosophers must examine the role played by deep learning as part of a wider
process of discovery. The philosophical distinction between the 'context of
discovery' and the 'context of justification' is helpful in this regard. I
demonstrate the importance of attending to this distinction with two cases
drawn from the scientific literature, and show that epistemic opacity need not
diminish AI's capacity to lead scientists to significant and justifiable
breakthroughs.

本文讨论深度神经网络的透明度带来的批判性和认识论挑战，并探讨了 AI 在科学中的作用，阐明了深度学习在发现过程中的重要作用，并结合两个科学案例阐释了 AI 在引领科学界取得重大和可证明突破方面的作用。